Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia

Abstract

We have discovered two genes, RSRC1 and ARHGAP18, associated with schizophrenia and in an independent study provided additional support for this association. We have both discovered and verified the association of two genes, RSRC1 and ARHGAP18, with schizophrenia. We combined a genome-wide screening strategy with neuroimaging measures as the quantitative phenotype and identified the single nucleotide polymorphisms (SNPs) related to these genes as consistently associated with the phenotypic variation. To control for the risk of false positives, the empirical P-value for association significance was calculated using permutation testing. The quantitative phenotype was Blood-Oxygen-Level Dependent (BOLD) Contrast activation in the left dorsal lateral prefrontal cortex measured during a working memory task. The differential distribution of SNPs associated with these two genes in cases and controls was then corroborated in a larger, independent sample of patients with schizophrenia (n=82) and healthy controls (n=91), thus suggesting a putative etiological function for both genes in schizophrenia. Up until now these genes have not been linked to any neuropsychiatric illness, although both genes have a function in prenatal brain development. We introduce the use of functional magnetic resonance imaging activation as a quantitative phenotype in conjunction with genome-wide association as a gene discovery tool.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

References

  1. McQueen MB, Devlin B, Faraone SV, Nimgaonkar VL, Sklar P, Smoller JW et al. Combined analysis from eleven linkage studies of bipolar disorder provides strong evidence of susceptibility loci on chromosomes 6q and 8q. Am J Hum Genet 2005; 77: 582–595.

    Article  CAS  Google Scholar 

  2. Vicente AM, Macciardi F, Verga M, Bassett AS, Honer WG, Bean G et al. NCAM and schizophrenia: genetic studies. Mol Psychiatry 1997; 2: 65–69.

    Article  CAS  Google Scholar 

  3. Kwasnicka-Crawford DA, Roberts W, Scherer SW . Characterization of an Autism-Associated Segmental Maternal Heterodisomy of the Chromosome 15q11-13 Region. J Autism Dev Disord 2006; 37: 694–702.

    Article  Google Scholar 

  4. Stein CM, Millard C, Kluge A, Miscimarra LE, Cartier KC, Freebairn LA et al. Speech sound disorder influenced by a locus in 15q14 region. Behav Genet 2006; 36: 858–868.

    Article  Google Scholar 

  5. Chagnon YC . Shared susceptibility region on chromosome 15 between autism and catatonia. Int Rev Neurobiol 2006; 72: 165–178.

    Article  Google Scholar 

  6. Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet 2002; 32: 650–654.

    Article  CAS  Google Scholar 

  7. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C et al. Complement factor H polymorphism in age-related macular degeneration. Science 2005; 308: 385–389.

    Article  CAS  Google Scholar 

  8. Hirschhorn JN, Daly MJ . Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005; 6: 95–108.

    Article  CAS  Google Scholar 

  9. Wang WY, Barratt BJ, Clayton DG, Todd JA . Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 2005; 6: 109–118.

    Article  CAS  Google Scholar 

  10. Liu B . Statistical Genomics: Linkage, Mapping, and QTL Analysis. CRC Press: Boca Raton, 1997.

    Google Scholar 

  11. Ebmeier K, Rose E, Steele D . Cognitive impairment and fMRI in major depression. Neurotox Res 2006; 10: 87–92.

    Article  CAS  Google Scholar 

  12. Dickerson BC . Functional MRI in the early detection of dementias. Rev Neurol (Paris) 2006; 162: 941–944.

    Article  CAS  Google Scholar 

  13. Yurgelun-Todd DA, Ross AJ . Functional magnetic resonance imaging studies in bipolar disorder. CNS Spectr 2006; 11: 287–297.

    Article  Google Scholar 

  14. Davis CE, Jeste DV, Eyler LT . Review of longitudinal functional neuroimaging studies of drug treatments in patients with schizophrenia. Schizophr Res 2005; 78: 45–60.

    Article  Google Scholar 

  15. Kircher TT, Thienel R . Functional brain imaging of symptoms and cognition in schizophrenia. Prog Brain Res 2005; 150: 299–308.

    Article  Google Scholar 

  16. Lawrie SM, Hall J, McIntosh AM, Cunningham-Owens DG, Johnstone EC . Neuroimaging and molecular genetics of schizophrenia: pathophysiological advances and therapeutic potential. Br J Pharmacol 2008; 153 (Suppl 1): S120–S124.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Turner JA, Smyth P, Macciardi F, Fallon JH, Kennedy JL, Potkin SG . Imaging phenotypes and genotypes in schizophrenia. Neuroinformatics 2006; 4: 21–49.

    Article  Google Scholar 

  18. Roffman JL, Weiss AP, Goff DC, Rauch SL, Weinberger DR . Neuroimaging-genetic paradigms: a new approach to investigate the pathophysiology and treatment of cognitive deficits in schizophrenia. Harv Rev Psychiatry 2006; 14: 78–91.

    Article  Google Scholar 

  19. Meyer-Lindenberg A, Zink CF . Imaging genetics for neuropsychiatric disorders. Child Adolesc Psychiatr Clin N Am 2007; 16: 581–597.

    Article  Google Scholar 

  20. Glahn DC, Thompson PM, Blangero J . Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Hum Brain Mapp 2007; 28: 488–501.

    Article  Google Scholar 

  21. Manoach DS . Prefrontal cortex dysfunction during working memory performance in schizophrenia: reconciling discrepant findings. Schizophr Res 2003; 60: 285–298.

    Article  Google Scholar 

  22. Barch DM, Carter CS, Braver TS, Sabb FW, MacDonald III A, Noll DC et al. Selective deficits in prefrontal cortex function in medication-naive patients with schizophrenia. Arch Gen Psychiatry 2001; 58: 280–288.

    Article  CAS  Google Scholar 

  23. Callicott JH, Egan MF, Mattay VS, Bertolino A, Bone AD, Verchinksi B et al. Abnormal fMRI response of the dorsolateral prefrontal cortex in cognitively intact siblings of patients with schizophrenia. Am J Psychiatry 2003; 160: 709–719.

    Article  Google Scholar 

  24. Karlsgodt KH, Glahn DC, van Erp TG, Therman S, Huttunen M, Manninen M et al. The relationship between performance and fMRI signal during working memory in patients with schizophrenia, unaffected co-twins, and control subjects. Schizophr Res 2007; 89: 191–197.

    Article  Google Scholar 

  25. Tura E, Turner JA, Fallon JH, Kennedy JL, Potkin SG . Multivariate analyses suggest genetic impacts on neurocircuitry in schizophrenia. Neuroreport 2008; 19: 603–607.

    Article  Google Scholar 

  26. Cannon TD, Keller MC . Endophenotypes in the genetic analyses of mental disorders. Annu Rev Clin Psychol 2006; 2: 267–290.

    Article  Google Scholar 

  27. Manoach DS, Press DZ, Thangaraj V, Searl MM, Goff DC, Halpern E et al. Schizophrenic subjects activate dorsolateral prefrontal cortex during a working memory task, as measured by fMRI. Biol Psychiatry 1999; 45: 1128–1137.

    Article  CAS  Google Scholar 

  28. Perlstein WM, Carter CS, Noll DC, Cohen JD . Relation of prefrontal cortex dysfunction to working memory and symptoms in schizophrenia. Am J Psychiatry 2001; 158: 1105–1113.

    Article  CAS  Google Scholar 

  29. Manoach DS, Gollub RL, Benson ES, Searl MM, Goff DC, Halpern E et al. Schizophrenic subjects show aberrant fMRI activation of dorsolateral prefrontal cortex and basal ganglia during working memory performance. Biol Psychiatry 2000; 48: 99–109.

    Article  CAS  Google Scholar 

  30. Akbarian S, Bunney Jr WE, Potkin SG, Wigal SB, Hagman JO, Sandman CA et al. Altered distribution of nicotinamide-adenine dinucleotide phosphate-diaphorase cells in frontal lobe of schizophrenics implies disturbances of cortical development. Arch Gen Psychiatry 1993; 50: 169–177.

    Article  CAS  Google Scholar 

  31. Papassotiropoulos A, Stephan DA, Huentelman MJ, Hoerndli FJ, Craig DW, Pearson JV et al. Common Kibra alleles are associated with human memory performance. Science 2006; 314: 475–478.

    Article  CAS  Google Scholar 

  32. Almasy L, Gur RC, Haack K, Cole SA, Calkins ME, Peralta JM et al. A genome screen for quantitative trait loci influencing schizophrenia and neurocognitive phenotypes. Am J Psychiatry 2008; 165: 1185–1192.

    Article  Google Scholar 

  33. Egan MF, Goldberg TE, Kolachana BS, Callicott JH, Mazzanti CM, Straub RE et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci USA 2001; 98: 6917–6922.

    Article  CAS  Google Scholar 

  34. Meyer-Lindenberg A, Nichols T, Callicott JH, Ding J, Kolachana B, Buckholtz J et al. Impact of complex genetic variation in COMT on human brain function. Mol Psychiatry 2006; 11: 867–877, 797.

    Article  CAS  Google Scholar 

  35. Glahn DC, Paus T, Thompson PM . Imaging genomics: mapping the influence of genetics on brain structure and function. Hum Brain Mapp 2007; 28: 461–463.

    Article  Google Scholar 

  36. de Geus E, Goldberg T, Boomsma DI, Posthuma D . Imaging the genetics of brain structure and function. Biol Psychol 2008; 79: 1–8.

    Article  Google Scholar 

  37. Aleman A, Swart M, van Rijn S . Brain imaging, genetics and emotion. Biol Psychol 2008; 79: 58–69.

    Article  Google Scholar 

  38. Shaw P, Gornick M, Lerch J, Addington A, Seal J, Greenstein D et al. Polymorphisms of the dopamine D4 receptor, clinical outcome, and cortical structure in attention-deficit/hyperactivity disorder. Arch Gen Psychiatry 2007; 64: 921–931.

    Article  Google Scholar 

  39. van Haren NE, Bakker SC, Kahn RS . Genes and structural brain imaging in schizophrenia. Curr Opin Psychiatry 2008; 21: 161–167.

    Article  Google Scholar 

  40. First MB, Spitzer RL, Gibbon M, Williams JBW . Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P). Biometrics Research New York State Psychiatric Institute: New York, 2002.

    Google Scholar 

  41. First MB, Spitzer RL, Gibbon M, Williams JBW . Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Non-patient Edition. (SCID-I/NP). Biometrics Research New York State Psychiatric Institute: New York, 2002.

    Google Scholar 

  42. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp 2000; 10: 120–131.

    Article  CAS  Google Scholar 

  43. Price A, Patterson N, Plenge R, Weinblatt M, Shadick N, DA R . Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 2006; 38: 904–909.

    Article  CAS  Google Scholar 

  44. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analysis. Am J Hum Genet 2007; 81: 559–575.

    Article  CAS  Google Scholar 

  45. StataCorp. Stata Statistical Software Release 10. Stata Corporation: College Station, Texas, 2007.

  46. Heimer L . Basal forebrain in the context of schizophrenia. Brain Res Brain Res Rev 2000; 31: 205–235.

    Article  CAS  Google Scholar 

  47. Petrides M . Lateral prefrontal cortex: architectonic and functional organization. Philos Trans R Soc Lond B Biol Sci 2005; 360: 781–795.

    Article  Google Scholar 

  48. Fallon JH, Opole IO, Potkin SG . Neuroanatomy of schizophrenia: circuitry and neurotransmitter systems. Clin Neurosci Res 2003; 3: 77–107.

    Article  CAS  Google Scholar 

  49. Potkin SG, Alva G, Fleming K, Anand R, Keator D, Carreon D et al. A PET study of the pathophysiology of negative symptoms in schizophrenia. Positron emission tomography. Am J Psychiatry 2002; 159: 227–237.

    Article  Google Scholar 

  50. Fallon J, Reid S, Kinyamu R, Opole I, Opole R, Baratta J et al. In vivo induction of massive proliferation, directed migration, and differentiation of neural cells in the adult mammalian brain. Proc Natl Acad Sci USA 2000; 97: 14686–14691.

    Article  CAS  Google Scholar 

  51. Nichols T, Hayasaka S . Controlling the familywise error rate in functional neuroimaging: a comparative review. Stat Methods Med Res 2003; 12: 419–446.

    Article  Google Scholar 

  52. Benjamini Y, Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B 1995; 57: 289–300.

    Google Scholar 

  53. Storey JD, Tibshirani R . Statistical significance for genomewide studies. Proc Natl Acad Sci USA 2003; 100: 9440–9445.

    Article  CAS  Google Scholar 

  54. Nyholt DR . A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 2004; 74: 765–769.

    Article  CAS  Google Scholar 

  55. Meng Z, Zaykin DV, Xu CF, Wagner M, Ehm MG . Selection of genetic markers for association analyses, using linkage disequilibrium and haplotypes. Am J Hum Genet 2003; 73: 115–130.

    Article  CAS  Google Scholar 

  56. Dudbridge F, Gusnanto A, Koeleman BP . Detecting multiple associations in genome-wide studies. Hum Genomics 2006; 2: 310–317.

    Article  CAS  Google Scholar 

  57. Balding DJ . A tutorial on statistical methods for population association studies. Nat Rev Genet 2006; 7: 781–791.

    Article  CAS  Google Scholar 

  58. Dudbridge F, Koeleman BP . Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. Am J Hum Genet 2004; 75: 424–435.

    Article  CAS  Google Scholar 

  59. Kirov G, Zaharieva I, Georgieva L, Moskvina V, Nikolov I, Cichon S et al. A genome-wide association study in 574 schizophrenia trios using DNA pooling. Mol Psychiatry 2008; DOI:10.1038/mp.2008.33.

    Article  Google Scholar 

  60. Lencz T, Morgan TV, Athanasiou M, Dain B, Reed CR, Kane JM et al. Converging evidence for a pseudoautosomal cytokine receptor gene locus in schizophrenia. Mol Psychiatry 2007; 12: 572–580.

    Article  CAS  Google Scholar 

  61. Mah S, Nelson MR, Delisi LE, Reneland RH, Markward N, James MR et al. Identification of the semaphorin receptor PLXNA2 as a candidate for susceptibility to schizophrenia. Mol Psychiatry 2006; 11: 471–478.

    Article  CAS  Google Scholar 

  62. Shifman S, Johannesson M, Bronstein M, Chen SX, Collier DA, Craddock NJ et al. Genome-wide association identifies a common variant in the reelin gene that increases the risk of schizophrenia only in women. PLoS Genet 2008; 4: e28.

    Article  Google Scholar 

  63. Sullivan PF, Lin D, Tzeng JY, van den Oord E, Perkins D, Stroup TS et al. Genomewide association for schizophrenia in the CATIE study: results of stage 1. Mol Psychiatry 2008; 13: 570–584.

    Article  CAS  Google Scholar 

  64. Plenge RM, Cotsapas C, Davies L, Price AL, de Bakker PI, Maller J et al. Two independent alleles at 6q23 associated with risk of rheumatoid arthritis. Nat Genet 2007; 39: 1477–1482.

    Article  CAS  Google Scholar 

  65. Takai Y, Sasaki T, Matozaki T . Small GTP-binding proteins. Physiol Rev 2001; 81: 153–208.

    Article  CAS  Google Scholar 

  66. Symons M . Rho family GTPases: the cytoskeleton and beyond. Trends Biochem Sci 1996; 21: 178–181.

    Article  CAS  Google Scholar 

  67. Kozma R, Ahmed S, Best A, Lim L . The Ras-related protein Cdc42Hs and bradykinin promote formation of peripheral actin microspikes and filopodia in Swiss 3T3 fibroblasts. Mol Cell Biol 1995; 15: 1942–1952.

    Article  CAS  Google Scholar 

  68. Wells A . EGF receptor. Int J Biochem Cell Biol 1999; 31: 637–643.

    Article  CAS  Google Scholar 

  69. Cao Q, Martinez M, Zhang J, Sanders AR, Badner JA, Cravchik A et al. Suggestive evidence for a schizophrenia susceptibility locus on chromosome 6q and a confirmation in an independent series of pedigrees. Genomics 1997; 43: 1–8.

    Article  CAS  Google Scholar 

  70. Zhang QH, Ye M, Wu XY, Ren SX, Zhao M, Zhao CJ et al. Cloning and functional analysis of cDNAs with open reading frames for 300 previously undefined genes expressed in CD34+ hematopoietic stem/progenitor cells. Genome Res 2000; 10: 1546–1560.

    Article  CAS  Google Scholar 

  71. Rakic S, Zecevic N . Early oligodendrocyte progenitor cells in the human fetal telencephalon. Glia 2003; 41: 117–127.

    Article  Google Scholar 

  72. Ge D, Zhang K, Need AC, Martin O, Fellay J, Urban TJ et al. WGAViewer: software for genomic annotation of whole genome association studies. Genome Res 2008; 18: 640–643.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This research was supported by grants to the Transdisciplinary Imaging Genetics Center (TIGC-P20 RR020837-01) and to the Functional Imaging Biomedical Informatics Research Network (FBIRN-1 U24 RR021992) from the National Center for Research Resources (NCRR) at the National Institutes of Health (NIH) and by grants POCEMON (FP7-ICT-2007-216088), FIRB Italia-Israele (RBIN04SWHR) and HYPERGENES (HEALTH-F4-2007-201550) and by an anonymous foundation. The Broad Institute Center for Genotyping and Analysis is supported by grant U54 RR020278-01 from the NCRR. We acknowledge the help and support of Mita Mancini and Yann Legros from Illumina, as well as Cristina Barlassina, Chiara Dal Fiume, Alessandro Orro and Federica Torri (University of Milan) for performing the HumanHap300 Bead Array procedures, as well as Liv Trondsen and Divya Rajpoot (UCI) for editorial support. We also acknowledge the recruitment, evaluation and SCID-based diagnostic assessment of healthy controls and schizophrenic subjects by FBIRN investigators: John Lauriello and Juan Bastillo, University of New Mexico; Daniel O'Leary, University of Iowa; Kelvin Lim, University of Minnesota; Gregory McCarthy, Judith Ford, Yale University; Arthur Toga, Tyrone Cannon, UCLA; Randy Gollub, Harvard University; Aysenil Belger, University of North Carolina; Dana Nguyen, Diane Highum, University of California, Irvine. We acknowledge the helpful comments of William E Bunney and Hal Stern.

Author information

Authors and Affiliations

Authors

Consortia

Corresponding author

Correspondence to S G Potkin.

Additional information

Author contributions: The fMRI task, imaging data from the discovery sample and imaging analyses for these results were programmed and implemented by Jessica Turner; the neuroanatomical and neuroscience expertize and genetic annotation was contributed by James Fallon; the genetic data analysis, PLINK and Eigenstrat analyses and genetic annotation were performed by Guia Guffanti and Fabio Macciardi; the in silico annotations were performed by Anita Lakatos; the visualization and gene viewer methods were developed by David Keator; the design and oversight of the experiments and analyses were the responsibility of Steven Potkin. Article preparation was a joint effort of all authors.

Supplementary Information accompanies the paper on the Molecular Psychiatry website (http://www.nature.com/mp)

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Potkin, S., Turner, J., Fallon, J. et al. Gene discovery through imaging genetics: identification of two novel genes associated with schizophrenia. Mol Psychiatry 14, 416–428 (2009). https://doi.org/10.1038/mp.2008.127

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/mp.2008.127

Keywords

This article is cited by

Search

Quick links